Gregor Kos

Mercury Physicochemical and Biogeochemical Transformation in the Atmosphere and at Atmospheric Interfaces: A Review and Future Directions

Atmosphere and at Atmospheric Interfaces: A Review and Future Directions Parisa A. Ariya,*,†,‡ Marc Amyot, Ashu Dastoor, Daniel Deeds,‡ Aryeh Feinberg,† Gregor Kos,‡ Alexandre Poulain, Andrei Ryjkov, Kirill Semeniuk, M. Subir, and Kenjiro Toyota †Department of Chemistry and ‡Department of Atmospheric and Oceanic Sciences, McGill University, 801 Sherbrooke Street West, Montreal, Quebec, Canada, H3A 2K6 Department of Biological Sciences, Universite ́ de Montreál, 90 avenue Vincent-d’Indy, Montreal, Quebec, Canada, H3C 3J7 Air Quality Research Division, Environment Canada, 2121 TransCanada Highway, Dorval, Quebec, Canada, H9P 1J3 Department of Biology, University of Ottawa, 30 Marie Curie, Ottawa, Ontario, Canada, K1N 6N5 Department of Chemistry, Ball State University, 2000 West University Avenue, Muncie, Indiana 47306, United States Air Quality Research Division, Environment Canada, 4905 Dufferin Street, Toronto, Ontario, Canada, M3H 5T4

Physical and chemical characterization of bioaerosols – Implications for nucleation processes

The importance of organic compounds in the oxidative capacity of the atmosphere, and as cloud condensation and ice-forming nuclei, has been recognized for several decades. Organic compounds comprise a significant fraction of the suspended matter mass, leading to local (e.g. toxicity, health hazards) and global (e.g. climate change) impacts. The state of knowledge of the physical chemistry of organic aerosols has increased during the last few decades. However, due to their complex chemistry and the multifaceted processes in which they are involved, the importance of organic aerosols, particularly bioaerosols, in driving physical and chemical atmospheric processes is still very uncertain and poorly understood. Factors such as solubility, surface tension, chemical impurities, volatility, morphology, contact angle, deliquescence, wettability, and the oxidation process are pivotal in the understanding of the activation processes of cloud droplets, and their chemical structures, solubilities and even the molecular configuration of the microbial outer membrane, all impact ice and cloud nucleation processes in the atmosphere. The aim of this review paper is to assess the current state of knowledge regarding chemical and physical characterization of bioaerosols with a focus on those properties important in nucleation processes. We herein discuss the potential importance (or lack thereof) of physical and chemical properties of bioaerosols and illustrate how the knowledge of these properties can be employed to study nucleation processes using a modeling exercise. We also outline a list of major uncertainties due to a lack of understanding of the processes involved or lack of available data. We will also discuss key issues of atmospheric significance deserving future physical chemistry research in the fields of bioaerosol characterization and microphysics, as well as bioaerosol modeling. These fundamental questions are to be addressed prior to any definite conclusions on the potential significance of the role of bioaerosols on physico-chemical atmospheric processes and that of climate.

Co-Occurrence of Regulated, Masked and Emerging Mycotoxins and Secondary Metabolites in Finished Feed and Maize—An Extensive Survey

Global trade of agricultural commodities (e.g., animal feed) requires monitoring for fungal toxins. Also, little is known about masked and emerging toxins and metabolites. 1926 samples from 52 countries were analysed for toxins and metabolites. Of 162 compounds detected, up to 68 metabolites were found in a single sample. A subset of 1113 finished feed, maize and maize silage samples containing 57 compounds from 2012 to 2015 from 44 countries was investigated using liquid chromatography and mass spectrometry. Deoxynivalenol (DON), zearalenone (ZEN) and fumonisins showed large increases of annual medians in Europe. Within a region, distinct trends were observed, suggesting importance of local meteorology and cultivars. In 2015, median DON concentrations increased to 1400 μg·kg−1 in Austria, but were stable in Germany at 350 μg·kg−1. In 2014, enniatins occurred at median concentrations of 250 μg·kg−1 in Europe, at levels similar to DON and ZEN. The latter were frequently correlated with DON-3-glucoside and ZEN-14-sulfate. Co-occurrence of regulated toxins was frequent with e.g., enniatins, and moniliformin. Correlation was observed between DON and DON-3-glucoside and with beauvericin. Results indicate that considerably more than 25% of agricultural commodities could be contaminated with mycotoxins as suggested by FAO, although this is at least partly due to the lower limits of detection in the current survey. Observed contamination percentages ranged from 7.1 to 79% for B trichothecenes and 88% for ZEN.

Optimisation of a sample preparation procedure for the screening of fungal infection and assessment of deoxynivalenol content in maize using mid-infrared attenuated total reflection spectroscopy

A sample preparation procedure for the determination of deoxynivalenol (DON) using attenuated total reflection mid-infrared spectroscopy is presented. Repeatable spectra were obtained from samples featuring a narrow particle size distribution. Samples were ground with a centrifugal mill and analysed with an analytical sieve shaker. Particle sizes of <100, 100–250, 250–500, 500–710 and 710–1000 µm were obtained. Repeatability, classification and quantification abilities for DON were compared with non-sieved samples. The 100–250 µm fraction showed the best repeatability. The relative standard deviation of spectral measurements improved from 20 to 4.4% and 100% of sieved samples were correctly classified compared with 79% of non-sieved samples. The DON level in analysed fractions was a good estimate of overall toxin content.

Validation of Chemometric Models for the Determination of Deoxynivalenol on Maize by Mid-Infrared Spectroscopy

Validation methods for chemometric models are presented, which are a necessity for the evaluation of model performance and prediction ability. Reference methods with known performance can be employed for comparison studies. Other validation methods include test set and cross validation, where some samples are set aside for testing purposes. The choice of the testing method mainly depends on the size of the original dataset. Test set validation is suitable for large datasets (>50), whereas cross validation is the best method for medium to small datasets (<50). In this study the K-nearest neighbour algorithm (KNN) was used as a reference method for the classification of contaminated and blank corn samples. A Partial least squares (PLS) regression model was evaluated using full cross validation. Mid-Infrared spectra were collected using the attenuated total reflection (ATR) technique and the fingerprint range (800–1800 cm−1) of 21 maize samples that were contaminated with 300 – 2600 µg/kg deoxynivalenol (DON) was investigated. Separation efficiency after principal component analysis/cluster analysis (PCA/CA) classification was 100%. Cross validation of the PLS model revealed a correlation coefficient of r=0.9926 with a root mean square error of calibration (RMSEC) of 95.01. Validation results gave an r=0.8111 and a root mean square error of cross validation (RMSECV) of 494.5 was calculated. No outliers were reported.

A novel chemometric classification for FTIR spectra of mycotoxin-contaminated maize and peanuts at regulatory limits

ABSTRACT The rapid identification of mycotoxins such as deoxynivalenol and aflatoxin B1 in agricultural commodities is an ongoing concern for food importers and processors. While sophisticated chromatography-based methods are well established for regulatory testing by food safety authorities, few techniques exist to provide a rapid assessment for traders. This study advances the development of a mid-infrared spectroscopic method, recording spectra with little sample preparation. Spectral data were classified using a bootstrap-aggregated (bagged) decision tree method, evaluating the protein and carbohydrate absorption regions of the spectrum. The method was able to classify 79% of 110 maize samples at the European Union regulatory limit for deoxynivalenol of 1750 µg kg–1 and, for the first time, 77% of 92 peanut samples at 8 µg kg–1 of aflatoxin B1. A subset model revealed a dependency on variety and type of fungal infection. The employed CRC and SBL maize varieties could be pooled in the model with a reduction of classification accuracy from 90% to 79%. Samples infected with Fusarium verticillioides were removed, leaving samples infected with F. graminearum and F. culmorum in the dataset improving classification accuracy from 73% to 79%. A 500 µg kg–1 classification threshold for deoxynivalenol in maize performed even better with 85% accuracy. This is assumed to be due to a larger number of samples around the threshold increasing representativity. Comparison with established principal component analysis classification, which consistently showed overlapping clusters, confirmed the superior performance of bagged decision tree classification. GRAPHICAL ABSTRACT

Portable Infrared Laser Spectroscopy for On-site Mycotoxin Analysis

Mycotoxins are toxic secondary metabolites of fungi that spoil food, and severely impact human health (e.g., causing cancer). Therefore, the rapid determination of mycotoxin contamination including deoxynivalenol and aflatoxin B1 in food and feed samples is of prime interest for commodity importers and processors. While chromatography-based techniques are well established in laboratory environments, only very few (i.e., mostly immunochemical) techniques exist enabling direct on-site analysis for traders and manufacturers. In this study, we present MYCOSPEC - an innovative approach for spectroscopic mycotoxin contamination analysis at EU regulatory limits for the first time utilizing mid-infrared tunable quantum cascade laser (QCL) spectroscopy. This analysis technique facilitates on-site mycotoxin analysis by combining QCL technology with GaAs/AlGaAs thin-film waveguides. Multivariate data mining strategies (i.e., principal component analysis) enabled the classification of deoxynivalenol-contaminated maize and wheat samples, and of aflatoxin B1 affected peanuts at EU regulatory limits of 1250 μg kg−1 and 8 μg kg−1, respectively.

Bio-Organic Materials in the Atmosphere and Snow: Measurement and Characterization

Bio-organic chemicals are ubiquitous in the Earths atmosphere and at air-snow interfaces, as well as in aerosols and in clouds. It has been known for centuries that airborne biological matter plays various roles in the transmission of disease in humans and in ecosystems. The implication of chemical compounds of biological origins in cloud condensation and in ice nucleation processes has also been studied during the last few decades, and implications have been suggested in the reduction of visibility, in the influence on oxidative potential of the atmosphere and transformation of compounds in the atmosphere, in the formation of haze, change of snow-ice albedo, in agricultural processes, and bio-hazards and bio-terrorism. In this review we critically examine existing observation data on bio-organic compounds in the atmosphere and in snow. We also review both conventional and cutting-edge analytical techniques and methods for measurement and characterisation of bio-organic compounds and specifically for microbial communities, in the atmosphere and snow. We also explore the link between biological compounds and nucleation processes. Due to increased interest in decreasing emissions of carbon-containing compounds, we also briefly review (in an Appendix) methods and techniques that are currently deployed for bio-organic remediation.

Using mid-infrared Fourier-Transform-Spectroscopy with attenuated total reflection (FT-IR/ATR) as a tool for the determination ofFusarium graminearum on maize

A novel method for the detection ofFusarium graminearum employing mid-infrared spectroscopy is described. In this study the fungus itself was determined on maize by pressing the ground sample against a diamond ATR-crystal, mounted horizontally in the sample chamber of an FT-IR spectrometer and recording the mid-infrared absorption spectrum. Multivariate data analysis was employed for the interpretation of spectra: Principal component analysis (PCA) was used to separate contaminated maize from uninfected samples. For concentrations higher than 8.23 mg/kg >80% of the samples were correctly classified with PCA. Spectra were correlated with HPLC results from reference measurements for ergosterol using principal component regression (PCR). A correlation coefficient of 0.9786 with an F-statistic of 107 was calculated (concentration range: 0.79–947 mg/kg ergosterol).

Classification of maize contaminated withFusarium Graminearum Using Mid-infrared Spectroscopy and Chemometrics

Advances in the development of a novel method for the detection ofFusarium graminearum employing mid-infrared spectroscopy are described. The dried and ground sample was sieved and the fraction with particle sizes between 250 and 100 μm was used for spectroscopy. The sample was pressed against a diamond ATR-crystal, mounted in the sample chamber of an FT-IR spectrometer and the mid-infrared absorption spectrum was recorded. The most prominent features in the spectrum were identified as protein, lipid and carbohydrate bands, which are changed by fungal infection. Multivariate data analysis was employed to detect the spectral changes: Principal component analysis (PCA) of mean centred data proved to be the most efficient method for the separation of contaminated maize from uninfected samples using the first two principal components. Ergosterol and the toxin deoxynivalenol (DON) served as reference parameters and were obtained from each sample using conventional analytical techniques. Samples with a toxin content of as low as 309 μg/kg could be separated from blank samples, which is well in the range of natural contamination. Investigated concentration ranges were 0.73–4.5 mg/kg for ergosterol and 0–2.6 mg/kg for DON. The percentage of correctly classified samples was between 75 and 100%.